code: def forward(self, x):
      residual = x
  
      out = self.conv1(x)
      out = self.bn1(out)
      out = self.relu(out)
  
      out = self.conv2(out)
      out = self.bn2(out)
      out = self.relu(out)
  
      out = self.conv3(out)
      out = self.bn3(out)
  
      if self.downsample is not None:
          residual = self.downsample(x)
  
      out += residual
      out = self.relu(out)
  
      return out
  
imports: from __future__ import print_function, division, absolute_import
  import torch.nn
  import math
  import torch.utils.model_zoo
  from torch.legacy import nn
  import torchfile
  from torch.utils.serialization import load_lua
  import torch
  import collections
  from PIL import Image
  import torchvision.transforms
label_desc: Use image node_id 2 for calling the Bottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out